Resolution modeling enhances PET imaging

Alessio, Adam M.; Rahmim, Arman; Orton, Colin G.

doi:10.1118/1.4821088

Cited by 26 publications

(16 citation statements)

References 21 publications

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“…In this case the resolution modelling is done in image space [13], but it can be also modelled in sinogram space or not modelled at all. The benefits and drawbacks of resolution modelling have been discussed in [14], [15]. A more accurate model for the system matrix could be obtained with Monte Carlo simulations, for example using GATE.…”

Section: Methodsmentioning

confidence: 99%

Composite system modelling for high accuracy brain PET image reconstruction using GATE

Belzunce

Reader

2016

2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/R

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Abstract-High resolution and good quantification is needed in specific regions of the brain in a number of PET brain imaging applications. An improvement in the spatial resolution and in the quantification of the tracer uptake can be achieved by using statistical reconstruction methods with an accurate model of the scanner acquisition process. This model is represented by a system response matrix and needs to include all the factors that contribute to the degradation of the reconstructed images. Monte Carlo simulations are the best method to model the complex physical processes involved in PET, but they have an extremely high computational cost and the system matrix needs to be recomputed for every new scan. Furthermore, for 3D PET the system matrix can have billions of elements, therefore at present it is impossible to store in memory during the iterative reconstruction. Consequently, on-the-fly Monte Carlo modelling of the system matrix has been previously proposed by other authors, where a Monte Carlo simulation is used in the forward projector and a simpler analytic model in the backprojector. In this work, we propose a different approach, where a composite system matrix is used, with a complete Monte Carlo model computed with GATE for a small subregion of the field of view and a simpler analytic model for the voxels outside that region. We evaluated the feasibility of the method using 2D simulations of a striatum phantom and a brain phantom. For each case, a Monte Carlo system matrix was generated with GATE for a subregion centred in the striatum. The brain simulations were reconstructed using the proposed method and compared with the standard reconstruction used clinically, with and without resolution modelling. For the striatum phantom, the use of a GATE system matrix showed an improvement of the reconstructed image, where a better definition of the structures in the striatum region was observed. For the case of the brain phantom, where the composite system matrix is used, an improvement was also observed but more limited compared with the pure GATE system matrix.

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Section: Methodsmentioning

confidence: 99%

Composite system modelling for high accuracy brain PET image reconstruction using GATE

Belzunce

Reader

2016

2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/R

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“…The latter usually shows an enhancement of the contrast and an improvement in the resolution of the reconstructed images . An apparent noise reduction is also observed, but at the cost of modifying the noise structure and introducing higher intervoxel correlations . Furthermore, resolution modeling suffers from Gibbs artifacts and can make the reconstruction problem under‐determined .…”

Section: Introductionmentioning

confidence: 99%

“…20 An apparent noise reduction is also observed, but at the cost of modifying the noise structure and introducing higher intervoxel correlations. 20,21 Furthermore, resolution modeling suffers from Gibbs artifacts and can make the reconstruction problem under-determined. 22 A possible reason for this effect is that the imaging system has an effective null space and therefore the higher frequencies cannot be properly recovered.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the impact of modeling axial compression on PET image reconstruction

Belzunce

Reader

2017

Medical Physics

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Purpose: To comprehensively evaluate both the acceleration and image-quality impacts of axial compression and its degree of modeling in fully 3D PET image reconstruction. Method: Despite being used since the very dawn of 3D PET reconstruction, there are still no extensive studies on the impact of axial compression and its degree of modeling during reconstruction on the end-point reconstructed image quality. In this work, an evaluation of the impact of axial compression on the image quality is performed by extensively simulating data with span values from 1 to 121. In addition, two methods for modeling the axial compression in the reconstruction were evaluated. The first method models the axial compression in the system matrix, while the second method uses an unmatched projector/backprojector, where the axial compression is modeled only in the forward projector. The different system matrices were analyzed by computing their singular values and the point response functions for small subregions of the FOV. The two methods were evaluated with simulated and real data for the Biograph mMR scanner. Results: For the simulated data, the axial compression with span values lower than 7 did not show a decrease in the contrast of the reconstructed images. For span 11, the standard sinogram size of the mMR scanner, losses of contrast in the range of 5-10 percentage points were observed when measured for a hot lesion. For higher span values, the spatial resolution was degraded considerably. However, impressively, for all span values of 21 and lower, modeling the axial compression in the system matrix compensated for the spatial resolution degradation and obtained similar contrast values as the span 1 reconstructions. Such approaches have the same processing times as span 1 reconstructions, but they permit significant reduction in storage requirements for the fully 3D sinograms. For higher span values, the system has a large condition number and it is therefore difficult to recover accurately the higher frequencies. Modeling the axial compression also achieved a lower coefficient of variation but with an increase of intervoxel correlations. The unmatched projector/backprojector achieved similar contrast values to the matched version at considerably lower reconstruction times, but at the cost of noisier images. For a line source scan, the reconstructions with modeling of the axial compression achieved similar resolution to the span 1 reconstructions. Conclusions: Axial compression applied to PET sinograms was found to have a negligible impact for span values lower than 7. For span values up to 21, the spatial resolution degradation due to the axial compression can be almost completely compensated for by modeling this effect in the system matrix at the expense of considerably larger processing times and higher intervoxel correlations, while retaining the storage benefit of compressed data. For even higher span values, the resolution loss cannot be completely compensated possibly due to an effective null space in the system. The u...

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“…In addition to increased spatial variation, PVC has also been shown to increase ensemble variation, which can lead to reduced reproducibility. 7,24,25 With the increasing interest in dose reduction for both PET/CT and SPECT/CT applications, along with advanced applications such as respiratory and/or cardiac gating 3 and dynamic SPECT imaging with tracer kinetic modeling, 26,27 the reconstructed images are already plagued with high level of noise as only a fraction of the counts are used. However, little work has been done on performing postreconstruction PVC while penalizing noise for the low counts applications mentioned above.…”

Section: Introductionmentioning

confidence: 99%

Noise suppressed partial volume correction for cardiac SPECT/CT

Chan

Liu

Grobshtein³

et al. 2016

Medical Physics

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Purpose: Partial volume correction (PVC) methods typically improve quantification at the expense of increased image noise and reduced reproducibility. In this study, the authors developed a novel voxel-based PVC method that incorporates anatomical knowledge to improve quantification while suppressing noise for cardiac SPECT/CT imaging. Methods: In the proposed method, the SPECT images were first reconstructed using anatomicalbased maximum a posteriori (AMAP) with Bowsher's prior to penalize noise while preserving boundaries. A sequential voxel-by-voxel PVC approach (Yang's method) was then applied on the AMAP reconstruction using a template response. This template response was obtained by forward projecting a template derived from a contrast-enhanced CT image, and then reconstructed using AMAP to model the partial volume effects (PVEs) introduced by both the system resolution and the smoothing applied during reconstruction. To evaluate the proposed noise suppressed PVC (NS-PVC), the authors first simulated two types of cardiac SPECT studies: a 99m Tc-tetrofosmin myocardial perfusion scan and a 99m Tc-labeled red blood cell (RBC) scan on a dedicated cardiac multiple pinhole SPECT/CT at both high and low count levels. The authors then applied the proposed method on a canine equilibrium blood pool study following injection with 99m Tc-RBCs at different count levels by rebinning the list-mode data into shorter acquisitions. The proposed method was compared to MLEM reconstruction without PVC, two conventional PVC methods, including Yang's method and multitarget correction (MTC) applied on the MLEM reconstruction, and AMAP reconstruction without PVC. Results: The results showed that the Yang's method improved quantification, however, yielded increased noise and reduced reproducibility in the regions with higher activity. MTC corrected for PVE on high count data with amplified noise, although yielded the worst performance among all the methods tested on low-count data. AMAP effectively suppressed noise and reduced the spill-in effect in the low activity regions. However it was unable to reduce the spill-out effect in high activity regions. NS-PVC yielded superior performance in terms of both quantitative assessment and visual image quality while improving reproducibility. Conclusions:The results suggest that NS-PVC may be a promising PVC algorithm for application in low-dose protocols, and in gated and dynamic cardiac studies with low counts. C 2016 American Association of Physicists in Medicine. [http://dx

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Resolution modeling enhances PET imaging

Cited by 26 publications

References 21 publications

Composite system modelling for high accuracy brain PET image reconstruction using GATE

Composite system modelling for high accuracy brain PET image reconstruction using GATE

Assessment of the impact of modeling axial compression on PET image reconstruction

Noise suppressed partial volume correction for cardiac SPECT/CT

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